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The reality of moving towards precision medicine
Elia Stupka discusses the advancement of precision medicine, emphasizing the need for personalized approaches and patient-centered research, particularly in the context of complex diseases and data integration. Projects like the IMI Aetionomy illustrate how cross-disease modeling and patient engagement can drive breakthroughs in understanding diseases such as idiopathic pulmonary fibrosis. The emphasis is on transitioning from statistical analysis to deep biological insights, thereby enhancing patient stratification and treatment precision.
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The reality of moving towards precision medicine
- 1. The reality ofmoving towards precision medicine Elia Stupka
- 2. Beyond Personalized Medicine:Participation and Precision
- 3. Diseasomes and pathwaysare here to stay! From common complex diseases to “multiple rare diseases” and “biological pathway clusters” From diseases to “diseasomes” From risk factor to “risk pattern” From clinical utility to “personal utility”
- 4. Diseasome driven example:IMI Project Aetionomy IMI project involving several pharmas and a dozen academic sites to model neurodegenerative disease and cross-disease models, e.g. synapse
- 5. Unique opportunities emergingfrom NGS scale-up Cheap Data Generation thanks to Accessible –OMICS Technologies Vast Medical Data Collection thanks to Patient Engagement technologies Complementing Big Data Approaches with High Throughput Experimental Validation Tackling Complex Disease: Link Detailed Subphenotypes to Specific Biology
- 6. Challenges: beyond interpretingsingle genomes…
- 7. Big Data: Seeingvs Looking I am looking for my biomarker in the right place and with the right tool, I know it’s the right one… With –OMICS we can “see” “everything” Need to know what you are looking for! Before knowing non- coding RNAs existed in the genome we were not “seeing” them
- 8. Genome Layers: morethan meets the eye Coding gene RNA-Seq: microRNAs, lncRNAs, alt. splicing… CAGE-Seq:TSSs DNA Methylation Chip-Seq /Histone Code Sequence Variants: SNPs, INDELs, CNVs, Structural Variants Allele Specific Expression Allele Specific Methylation
- 9. Moving from statisticsto deep biology, and then back to statistics Move Away from N=1000 Approach for Research Move towards: Understand How THIS Gene/Pathway in THIS Patient is causing THIS Phenotype AND ONLY THEN: Go back to 10,000s of well defined patients who can benefit from drug targeted to specific biology!
- 10. Moving towards understandingof single phenotypes through combination of NGS, HCS, Comp Bio and Brains!
- 11. Example of deepsingle patient work
- 12. Getting the righttarget, a fitting example: our collaboration with Circuit Therapeutics • Thanks to this collaboration we are able to explore SINGLE CELLS IN SINGLE CIRCUITS RESPONSIBLE FOR SPECIFIC BEHAVIOURAL PHENOTYPES • Combining the power of NGS with the power of Optogenetics helps us shed light on specific genes and their role in these circuits that would otherwise be lost in the complexity of the brain • We can compare „same“ cell types in different circuits
- 13. Mining the IGNOROME •No differences in expression between ignorome genes and well known genes • Main difference is how early they were discovered, and moved onto the „bandwagon“ • Let‘s get off the bandwagon! • In our first dataset we have a few dozen „ignorome“ genes!
- 14. How about puttingpatients at the CENTER of the whole research and development process? BI initiated a partnership with Patients Like me to develop a community for IPF: Idiopathic Pulmonary Fibrosis, a rare and rapidly progressive diseae (from diagnosis to death: 3 years) In the meantime, a few weeks ago, BI had the first drug ever approved for IPF on the market: Ofev (Nintedanib) This drug will lengthen significantly the life of IPF patients, but we can do much more, if we understand the basic mechanisms that lead to fibrosis The partnership is a unique, and much deeper way, to engage with patients, their symptoms, and understand their progression
- 15. Over 2,500 patientsenrolled!
- 16. In one year,more patients than any current trial
- 17. The Patient-Centric DataOnion Online Virtual Community self-reported information Integration of clinical data (HER + Clinics) Collection of patient samples and live wearable data We can move from online to the real world, with samples and wearables and develop a much deeper view of each patient
- 18. Where can thistake us? Systems Modelling of Disease Progression, DAY by DAY Engaging very specific patients for research projects integrating clinical health records, imaging data, genomics profiles. Much clearer patient stratification, phenotype sublcuster analysis Clearer understand of REAL LIFE experiences of the disease, specific symptoms which cause distress, unmet medical needs, etc.
- 19. Moving towards precisionmedicine PRECISE BIOLOGY (e.g. Optogenetics) PRECISE PATIENTS (e.g. Patients Like Me) PRECISION MEDICINE
- 20. The „Ecosystem“ beyondsequencing Xten, GEL, etc provide unprecedented Seq capacity There are important efforts, public and private, to make downstream interpretation easier and cheaper What about UPSTREAM? New bottlenecks: biobanking, consent, logistics, homogenous clinical data
- 21. The big buzzword:big data to transform healthcare…
- 22. WHAT: overview ofthe varied landscape of big data in health 22 Source: Weber G, Mandl K, Kohane I. Finding the Missing Link for Big Biomedical Data. Journal of the American Medical Association, May 22, 2014. doi:10.1001/jama.2014.4228. Authored by the CTO of Harvard Medical School.
- 23. In 2010, GEwas risking to go out of business, due to its old business model: sell hardware and repair it, risking to become a commodity provider. The money was moving to IBM, SAP and big-data start-ups.These companies were proposing to provide data and analytics on data from GE equipment to derive efficiency and other benefits In 2011 GE initiated a multibillion dollar investment towards the industrial internet: turn every instrument into into a cloud- connected communicating device and opening a CA based big data focused site Now GE doesn‘t sell jet engines, it sells business efficiency (more miles flown, less downtime) based on cloud-enables real-time decision making. The General Electrics Case Study PDF
- 24. Business model changeat GE PHARMA: • Use data and analytics to prodive real-time decision support for targets, biomarkers, clinical trials, and markets • Optimize and expand customer, i.e. patient outcomes • Optimize assets (i.e. patient-derived data) and operations (i.e. where and how knowledge is utilized
- 25. GE was notagile, responsive and strategically coherent in the computational area In 2011 all IT/software/data efforts were scattered and fragmented, with 12,000 software professionals working based on local requirements, frameworks, technologies, vendors, etc. A new head was brought in, a new center built in California, many new, highly talented people brought in, agile with latest technologies and big data approaches. 150 people in 2013, 1,000 end of 2014. Result: 1B$ incremental income in 2014 Another example: NEST, the digital cloud-enabled thermostat, bought by Google for 3.2B$ The „only“ difference with a normal thermostat is that the data in the cloud enables companies to drive efficiency, reduce cost, increase profit. Where did the GE investment go?
- 26. Knowledge, in 2015,an asset as valuable as compounds? &
- 27. Patient profile • Earlysignals from wearable devices • Genetics • Who owns the data? Apple, Google, Patients Like Me, 23andMe? Diagnosis • Hospital general diganosis, precise biomarker driven diagnosis? • Who owns the data? Illumina? Theranos working directly with Wallgreens as one stop shop Disease Modelling • Choice of environmental, lifestyle, natural, etc. options. Eefficacy, treatment switching and treatment combination, • Who owns the data? Patients Like Me? Drug/Biomarker • Drug approved, for specific subset of patients • Owned by Pharma, sometimes with proprietary biomarker ???? • Who can drive an „ecosystem“ to connect the dots from early symptoms to precise medicine? • A 21st century hospital? A technology giant like Apple or Google? Or a 21st century Pharma? CONTEXT: key challenge in health: owning the interpretation power to move from data to precise diagnosis to precise cure
- 28. Are we movingtowards an App Store of Research Data?
- 29. Elia Stupka –[email protected] Thank You!